To effectively harness their power, an AI prompt engineering course is essential.. In last couple years, the rise of Large Language Models (LLMs) like ChatGPT & Claude as intuitive and usable products has opened up a world of possibilities for normal people around the globe to have AI-powered solutions at their fingertips. However, just like in most areas of life, garbage in, garbage out. Effectively harnessing the power of these models requires a deeper understanding of prompt engineering – the art and science of designing input prompts that elicit desired outputs from LLMs.
While I’ve ad a number of articles about prompt engineering, and fancy myself a prompt engineer, I recently decided to take a specialization course on Coursera called Prompt Engineering for ChatGPT. The course is offered by Dr. Jules White of Vanderbilt University. Below I overview my experience, learnings, and overall impressions of the course.
Course Overview: AI Prompt Engineering Specialization
The Prompt Engineering Specialization, offered by Coursera and Vanderbilt University, is a comprehensive course designed to teach learners how to effectively utilize Large Language Models (LLMs) through prompt engineering. The course covers various techniques and patterns for creating effective prompts, enabling users to more fully unlock the potential of LLMs for a wide range of applications. No prior computer science background is required, as the course focuses understanding how LLMs function and then training you for better logical writing to achieve your aims.
Overall I found this course extremely helpful, though a little slow paced, it gave me a set of concepts to work with in creating my own prompts, helped me to understand LLMs a bit better, and I utilized the lessons and some of the academic studies the course referenced to create my own GPT-Assistant “Prompt Engineering Optimizer“. I use this GPT on a daily basis by calling it into my chats whenever I need to write a fairly complex prompt.
Impressions
The Prompt Engineering Specialization provides a solid foundation for beginners looking to understand and leverage the power of LLMs. The course material is well-structured, with each week focusing on specific concepts and techniques. The instructor presents the information in a clear and engaging manner, making the content accessible to learners from diverse backgrounds. However, the course might not offer significant value to those already experienced in working with LLMs and prompt engineering, unless you are the type who values understanding practical skills from an academic perspective.
Course Content
The course covers a wide range of topics related to prompt engineering, including:
- Understanding LLMs and their capabilities
- Randomness in output and handling unexpected results
- Creating effective prompts using patterns such as Persona, Audience Persona, and Flipped Interaction
- Using few-shot examples, chain-of-thought prompting, and ReAct prompting
- Applying advanced techniques like the Game Play, Template, and Meta Language Creation patterns
- Combining patterns for more powerful prompts and creating fact-checking and semantic filters
Personal Insights and Takeaways: Mastering Prompt Engineering Techniques for AI
The most useful aspects of the course is its introduction of various prompt patterns, which serve as templates or frameworks for constructing prompts that elicit desired responses from LLMs. These patterns can be combined with others to form master patterns, which I’ve done in the implementation of Customizing my ChatGPT System Instructions. There are a number of prompts from this course which I now use on a daily basis.
Among the prompt patterns covered, I found the following particularly intriguing:
- Persona Pattern: This pattern involves instructing the LLM to adopt a specific persona or role, such as an expert in a particular field or a character with a distinct perspective. By using the Persona Pattern, users can effectively “phone an expert” and obtain responses tailored to the specified persona’s knowledge and viewpoint. This pattern has numerous potential applications, from generating targeted content to facilitating role-playing and scenario-based learning.
- Menu Actions Pattern: This pattern involves creating a “menu” of specific actions or functions that the LLM can perform based on user input. It allows for the development of more interactive and dynamic prompts, enabling users to access different functionalities or modes within a single conversation. By combining the Menu Actions Pattern with other techniques, such as the Template Pattern for structuring output, users can create powerful and versatile prompts for a wide range of applications.
- Few-shot examples: These demonstrate the desired format or style of the output.
- Chain-of-thought prompting: This encourages the LLM to explain its reasoning step-by-step.
- Question Refinement Pattern: This pattern suggests better versions of the user’s question to improve the accuracy and relevance of the LLM’s response.
- Alternative Approaches Pattern: This pattern generates alternative ways to accomplish the same task, allowing users to compare and select the most suitable approach.
For those more in a time crunch and wanting to get up to speed quickly I think reviewing the ChatGPT Prompt Engineering Guide as well as the Anthropic Prompt Engineering Guide will enable you to improve your prompt engineering greatly.
Future Improvements
While I recommend the course, I do think there is room for improvements. The instructor could consider including more hands-on exercises or projects that allow learners to apply the techniques covered in the course to more specific use cases. Additionally, the course could benefit from more diverse and practical examples, showcasing the potential applications of prompt engineering across various domains and industries. Finally, incorporating a discussion forum or other means of fostering community engagement could help learners connect with and learn from one another.
Conclusion: Is the Coursera Prompt Engineering Course Worth It?
The Prompt Engineering Specialization provides a valuable introduction to the world of prompt engineering and LLMs. While it may not offer significant new insights for experienced practitioners, the course serves as an excellent starting point for beginners looking to understand and apply these techniques in their own projects. With clear explanations, a wide range of topics, and practical examples, the course equips learners with the tools and knowledge needed to effectively leverage LLMs through prompt engineering.
Overall, the Prompt Engineering Specialization provides a solid foundation for anyone looking to harness the power of LLMs through well-crafted prompts. By offering a comprehensive set of prompt patterns, best practices, and real-world examples, the course equips learners with the tools and knowledge needed to unlock the full potential of LLMs in their projects and workflows. As I continue to explore and experiment with prompt engineering, I look forward to applying these valuable insights and techniques in my own work.
